Benchmark: lodash groupBy vs Array.reduce vs Object.groupBy 100k

HTML Preparation code:


<script src='https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.5/lodash.min.js'></script>

Script Preparation code:

var max2 = 100000;

var data = [];
for (var i = 0; i <= max2; i++) { data.push({ id: i }); }

Tests:

Lodash
_.groupBy(data, ({ id }) => id)
NativeReduce
data.reduce((acc, item) => { acc[item.id] = item; return acc; }, {})
NativeGroupBy
Object.groupBy(data, ({ id }) => id)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Lodash
NativeReduce
NativeGroupBy

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 2 months ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:134.0) Gecko/20100101 Firefox/134.0

Browser/OS: Firefox 134 on Mac OS X 10.15

View result in a separate tab

Test name	Executions per second
Lodash	908.1 Ops/sec
NativeReduce	2124.5 Ops/sec
NativeGroupBy	910.1 Ops/sec

Autogenerated LLM Summary (model llama3.2:3b, generated 6 months ago):

LLMs can make mistakes. Check important info.

Let's break down the benchmark and its test cases.

**What is tested:**

The provided JSON represents three microbenchmarks that test different approaches to grouping data by an identifier. The benchmarks measure the execution time of each approach:

1. **Lodash groupBy**: Uses the Lodash library (`lodash.js`) to implement a `groupBy` function.
2. **NativeReduce**: A native JavaScript implementation using the `reduce` method.
3. **Object.groupBy** (native): Another native JavaScript implementation that uses the `groupBy` method on objects.

**Options compared:**

The benchmarks compare the execution time of each approach:

* Lodash groupBy
* NativeReduce
* Object.groupBy (native)

Each benchmark is tested with a large dataset (`data`) containing 100,000 objects, where each object has an `id` property. The `max2` variable determines the upper limit of the dataset.

**Pros and Cons:**

1. **Lodash groupBy**:
	* Pros: Lodash provides a convenient and tested implementation that abstracts away the complexity of grouping data.
	* Cons: Requires including the Lodash library, which may add unnecessary overhead to the test.
2. **NativeReduce**:
	* Pros: Native JavaScript implementation allows for better performance and control over the algorithm.
	* Cons: Requires manual implementation of the grouping logic, which can be error-prone and less efficient than the optimized Lodash implementation.
3. **Object.groupBy (native)**:
	* Pros: Similar to NativeReduce, but uses a more straightforward approach by directly using the `groupBy` method on objects.
	* Cons: May not be as performant or flexible as NativeReduce.

**Library usage:**

The `lodash.js` library is used in the Lodash groupBy benchmark. The library provides a convenient implementation of the `groupBy` function, which abstracts away the complexity of grouping data.

**Special JS feature/syntax:**

None mentioned in this benchmark. However, if there were special features or syntax, they would likely be related to JavaScript's functional programming capabilities, such as arrow functions (`=>`) used in some benchmarks.

**Alternatives:**

Other approaches for grouping data could include:

1. **D3.js**: A popular data visualization library that provides a `groupBy` function.
2. **Ramda**: Another functional programming library that includes a `groupBy` function.
3. **Manual implementation**: Implementing the grouping logic from scratch, without relying on built-in methods or libraries.

Keep in mind that these alternatives may offer different performance characteristics, trade-offs, and use cases compared to the Lodash groupBy, NativeReduce, and Object.groupBy (native) implementations tested in this benchmark.

Related benchmarks:

lodash groupBy vs Array.reduce vs Object.groupBy 100k (version: 0)

Comparing performance of: Lodash vs NativeReduce vs NativeGroupBy

Created: one year ago by: Guest

Jump to the latest result

Lodash

NativeReduce

NativeGroupBy

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 6 months ago):